{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "714855ed-9040-44b1-a930-86edf0952277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4428241dd396428683892fe51ed5d438",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "device = \"cuda\" # the device to load the model onto\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    \"/home/lyz/hf-models/Qwen/Qwen1.5-4B-Chat/\",\n",
    "    torch_dtype=\"auto\",\n",
    "    device_map=\"auto\"\n",
    ")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"/home/lyz/hf-models/Qwen/Qwen1.5-4B-Chat/\")\n",
    "\n",
    "prompt = \"hello\"\n",
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
    "    {\"role\": \"user\", \"content\": prompt}\n",
    "]\n",
    "text = tokenizer.apply_chat_template(\n",
    "    messages,\n",
    "    tokenize=False,\n",
    "    add_generation_prompt=True\n",
    ")\n",
    "model_inputs = tokenizer([text], return_tensors=\"pt\").to(device)\n",
    "\n",
    "generated_ids = model.generate(\n",
    "    model_inputs.input_ids,\n",
    "    max_new_tokens=512\n",
    ")\n",
    "generated_ids = [\n",
    "    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)\n",
    "]\n",
    "\n",
    "response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "579d0f7f-a511-4d53-9b6c-a4cd1fcc2b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "test = pd.read_csv('./test_input.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a06baea3-940e-4a14-885b-b8175075efdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "for test_prompt in test[0].values:\n",
    "    prompt = f\"列举与下面句子最相关的五个成语。只需要输出五个成语，不需要有其他的输出，写在一行中：{test_prompt}\"\n",
    "\n",
    "    words = ['同舟共济'] * 5\n",
    "    for _ in range(10):\n",
    "        messages = [\n",
    "            {\"role\": \"user\", \"content\": prompt},\n",
    "        ]\n",
    "        text = tokenizer.apply_chat_template(\n",
    "            messages,\n",
    "            tokenize=False,\n",
    "            add_generation_prompt=True\n",
    "        )\n",
    "        model_inputs = tokenizer([text], return_tensors=\"pt\").to(device)\n",
    "        \n",
    "        generated_ids = model.generate(\n",
    "            model_inputs.input_ids,\n",
    "            max_new_tokens=512\n",
    "        )\n",
    "        generated_ids = [\n",
    "            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)\n",
    "        ]\n",
    "        \n",
    "        response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
    "        response = response.replace('\\n', ' ').replace('、', ' ')\n",
    "        words = [x for x in response.split() if len(x) == 4 and x.strip() != '']\n",
    "        if len(words) == 5:\n",
    "            break\n",
    "\n",
    "\n",
    "    if len(' '.join(words).strip()) != 24:\n",
    "        words = ['同舟共济'] * 5\n",
    "\n",
    "    with open('submit.csv', 'a+') as up:\n",
    "        up.write(' '.join(words) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e852a9a-6aae-4bfa-b18b-030e593b1e77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len('一模一样 如出一辙 千篇一律 大同小异 毫无二致')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60d6f9b8-8f12-40bc-baca-27198dd4989d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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